Our research covers the transmission of sound with hearing devices. Our goal is to understand and improve this process by using technological innovations.
And our introductory article on Smart Hearing Devices:
Perception & Cognition
This research investigates different input modalities of sound transmission (acoustic, tactile, electric) and their combinations. We use acoustic simulations of sound perception with cochlear implants and assess whether integration of tactile and auditory input helps CI listening. We also investigate whether hearable devices live up to their claims of helping people with hearing loss.
This multi-centre project led by Dr Mark Fletcher at the University of Southampton investigates the combination of tactile and auditory stimulation to enhance speech perception with cochlear implants. A prototype device is being developed.
This collaboration with Dr Saima Rajasingam at Anglia Ruskin University is a series of investigations into listening performance with and attitudes towards Hearables for people with mild-to-moderate hearing loss.
We focus on improving speech signals before they are presented to the listener (pre-processing), for example by removing background noise or other acoustic interferences. We use powerful methods from deep learning (deep neural networks) and digital signal processing (adaptive filters) to facilitate speech perception with hearing devices.
Speech enhancement based on DEEP neural networks
We develop noise-reduction algorithms based on deep neural networks (DNNs), to enhance speech perception in noisy situations. The DNNs are optimised with many thousand examples of noisy speech and then evaluated in listening studies with cochlear implant and hearing aid users.
Ongoing work includes:
Evaluation of multi-microphone algorithms (Led by Dr Clément Gaultier)
Development of speaker-aware algorithms (Led by Iordanis Thoidis)
Optimisation for cochlear implant speech processing
SPEECH ENHANCEMENT WITH ADAPTIVE FILTERING
In this collaboration with Dr Alan Archer-Boyd and Dr Charlotte Garcia we developed an adaptive algorithm to filter-out interfering background sounds in realistic situations (e.g. in a coffeeshop).
In practice, obtaining a reference signal via streaming could facilitate speech perception.
These projects investigate the electro-neural interface and stimulation patterns with cochlear implants and their impact on speech perception. We develop new coding strategies, assess channel interaction effects and build computational models for the electrical stimulation and sound transmission with cochlear implants.
Speech coding strategies for cochlear implants
We develop novel speech coding strategies to improve speech perception with cochlear implants:
TIPS: Temporal integrator processing strategy (Project led by Dr Lidea Shahidi)
A novel strategy for cochlear implants to improve speech perception in noise and to reduce power consumption. We are improving the robustness to various acoustic scenarios and are developing a real-time implementation.
Spectral blurring in cochlear implants
In this project with Dr Bob Carlyon (CBU) and Prof Julie Arenberg (Harvard) we manipulate channel interaction with cochlear implants to assess its effects on speech perception. Our findings guide future development of speech processing strategies and clinical assessment.
end-2-end models of COCHLEAR IMPLANT STIMULATION
Project led by Dr Tim Brochier with Dr Josef Schlittenlacher, Dr Iwan Roberts, Dr Chen Jiang, Dr Debi Vickers and Prof Manohar Bance.
We have built high-resolution computational models in combination with automatic speech recognition to assess information transmission with cochlear implants.
patient-specific cochlear implant stimulation PATTERNS
Project led by Dr Charlotte Garcia to develop a model-based algorithm (PECAP) for estimating patient-specific excitation profiles to characterise stimulation and neural health patterns.
The PECAP algorithm is currently used by several clinics and follow-on projects
The PECAP algorithm has recently been made faster ("SpeedCAP", Garcia et al. 2022)
The PECAP algorithm successfully detected blurred stimulation and neural dead regions
Perception & cognition
We assess different aspects of speech perception. This includes signal qualities, such as spectral and temporal resolution, and speech perception in terms of intelligibility, quality and listening effort as well as through electrophysiological EEG measures.
Project led by Dr Alan Archer-Boyd and Dr Bob Carlyon.
Investigations of spectro-temporal resolution with cochlear implants using the STRIPES test.
We published a new online version of the STRIPES test (Archer-Boyd et al. 2022)
Speech perception: Listening experiments
We use a range of measures for assessing speech perception, such as measuring speech intelligibility, speech quality and tolerance thresholds for distortions and artefacts.
Our new online test system, AUDITO, is currently being finalised and will be used to perform online listening experiments with cochlear implant users.
Speech transmission index: Neural entrainment
In this project led by Dr Alexis Deighton MacIntyre we use electrophysiological markers via EEG measurements to assess speech entrainment and develop objective indices of perception.
We developed a novel listening paradigm to overcome limitations of previous approaches.